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Titlebook: Machine Vision and Augmented Intelligence—Theory and Applications; Select Proceedings o Manish Kumar Bajpai,Koushlendra Kumar Singh,George

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發(fā)表于 2025-3-23 09:42:19 | 只看該作者
Astha Verma,Vijay Bhaskar Semwal,Koushlendra Kumar Singhsuch a systemof equations is called a .,while the individual solutions are the . or . of the system. In this book, we consider time systems, where the trajectories are functions of a continuous or discrete-time variable which satisfy linear differential or difference equations with constant coeffici
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發(fā)表于 2025-3-23 16:46:08 | 只看該作者
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發(fā)表于 2025-3-23 22:12:55 | 只看該作者
Sanjay Kumar,Binod Kumar Singherts in this field.The aim of this book is to propose a new approach to analysis and control of linear time-varying systems.? These systems are defined in an intrinsic way, i.e., not by a particular representation (e.g., a transfer matrix or a state-space form) but as they are actually.? The system
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發(fā)表于 2025-3-24 05:24:34 | 只看該作者
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發(fā)表于 2025-3-24 09:26:57 | 只看該作者
Ayushi Rastogi,Ujjayanta Bhoumik,Chhavi Choudhary,Akbar Sheikh Akbari,Koushlendra Kumar Singherts in this field.The aim of this book is to propose a new approach to analysis and control of linear time-varying systems.? These systems are defined in an intrinsic way, i.e., not by a particular representation (e.g., a transfer matrix or a state-space form) but as they are actually.? The system
17#
發(fā)表于 2025-3-24 13:23:39 | 只看該作者
Sunil Tiwari,Manish Bhardwaj,Rakesh Kumar Katareway, i.e., not by a particular representation (e.g., a transfer matrix or a state-space form) but as they are actually.? The system equations, derived, e.g., from the laws of physics, are gathered to form an intrinsic mathematical object, namely a finitely presented module over a ring of operators.?
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發(fā)表于 2025-3-24 16:51:34 | 只看該作者
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發(fā)表于 2025-3-24 22:28:25 | 只看該作者
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發(fā)表于 2025-3-25 00:22:01 | 只看該作者
Stock Market Predictions Using FastRNN, CNN, and Bi-LSTM-Based Hybrid Model,op deep learning-based hybrid model for live predictions of stock values. The proposed model is a hybrid deep learning model, utilizing the best features of Fast Recurrent Neural Networks (FastRNN), Convolutional Neural Networks (CNN), and Bi-Directional Long Short-Term Memory (Bi-LSMT) models, to p
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